International Journal of Machine Learning and Cybernetics

Aims & Scope

Cybernetics is concerned with describing complex interactions and interrelationships between systems which are omnipresent in our daily life.

Machine Learning discovers fundamental functional relationships between variables and ensembles of variables in systems.

The merging of the disciplines of Machine Learning and Cybernetics is aimed at the discovery of various forms of interaction between systems through diverse mechanisms of learning from data.

The International Journal of Machine Learning and Cybernetics (IJMLC) focuses on the key research problems emerging at the junction of machine learning and cybernetics and serves as a broad forum for rapid dissemination of the latest advancements in the area.

The emphasis of IJMLC is on the hybrid development of machine learning and cybernetics schemes inspired by different contributing disciplines such as engineering, mathematics, cognitive sciences, and applications.

New ideas, design alternatives, implementations and case studies pertaining to all the aspects of machine learning and cybernetics fall within the scope of the IJMLC.

Key research areas to be covered by the journal include: -Machine Learning for modeling interactions between systems -Pattern Recognition technology to support discovery of system-environment interaction -Control of system-environment interactions -Biochemical interaction in biological and biologically-inspired systems -Learning for improvement of communication schemes between systems

View Aims & Scope

---

Metrics & Ranking

Impact Factor

Year	Value
2025	2.7
2024	3.10

Journal Rank

Year	Value
2024	8190

Journal Citation Indicator

Year	Value
2024	2890

SJR (SCImago Journal Rank)

Year	Value
2024	0.694

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	73

---

---

Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

---

Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

---

Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Extreme learning machines: a survey

  Citation: 1660

  Authors: Guang-Bin, Dian Hui, Yuan

• Understanding bag-of-words model: a statistical framework

  Citation: 1031

  Authors: Yin, Rong, Zhi-Hua

• A survey on federated learning: challenges and applications

  Citation: 447

  Authors: Jie, Zhixia, Yang, Zhihua, Jianghui, Wensheng

• A review of hand gesture and sign language recognition techniques

  Citation: 421

  Authors: Ming Jin, Zaid, Mohamed Hisham

• Gaining-sharing knowledge based algorithm for solving optimization problems: a novel nature-inspired algorithm

  Citation: 414

  Authors: Ali Wagdy, Anas A., Ali Khater

• Recent advances in deep learning

  Citation: 252

  Authors: Xizhao, Yanxia, Farhad

• Adaptive guided differential evolution algorithm with novel mutation for numerical optimization

  Citation: 247

  Authors: Ali Wagdy, Ali Khater

• A survey on application of machine learning for Internet of Things

  Citation: 242

  Authors: Laizhong, Shu, Fei, Zhong, Nan, Jing

• Linear discriminant analysis for the small sample size problem: an overview

  Citation: 215

  Authors: Alok, Kuldip K.

• Study on the prediction of stock price based on the associated network model of LSTM

  Citation: 211

  Authors: Guangyu, Liangxi

---